ICRA 2018 RL Occluded Intersection - Honda Research Institute USA

ICRA 2018 RL Occluded Intersection

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

David Isele Reza Rahimi Akansel Cosgun Kaushik Subramanian Kikuo Fujimura

International Conference on Robotics and Automation (ICRA) 2018

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate, and have limited ability to generalize. We then explore a system’s ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.

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